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Due to the advancement of digital media, a large number of electronic books are digitized from old paper books through digital cameras or scanners. The scanned image often contains the distractions such as noises outside the page boundary, skewed pages, and irregular distributions of image illumination that may degrade the quality of scanned images. As for this paper, we propose an alternative algorithm...
This paper presents a performance evaluation of two different sub-pixel motion estimation algorithms, one base on Block-Matching and the other based on optical flow to obtain sub-pixel displacement. On block-matching, we focus on block-based full search (FS), three step search (TSS), two dimensional logarithm search (TDL), cross search algorithm (CSA), a new three step search algorithm (NTSS), a novel...
Traditional Super-Resolution Reconstruction (SRR) vigorously falls back on the availability of accurate registration for this fusion task and the observation noise model. When the motion is registered inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the super-resolved outcome and when the observation noise is not AWGN, severe artifacts appear in the reconstructed...
Due to large data set, block processing is usually applied for fast compressive sensing (CS) reconstruction; however, it gives the undesired blocking artifact in reconstructed data. In order to reduce blocking artifact and preserve high frequency, this paper proposes a novel block processing on wavelet domain instead of spatial domain. No post-processing nor special mapping is included. CS is applied...
In this paper, we propose a alternative robust video enhancement algorithm using SRR based on the regularization ML technique. First, the classical registration process is used to estimate the relationship between the reference frame and other neighboring frames. Subsequently, the Andrew's Sine norm is used for measuring the difference between the projected estimate of the high quality image and each...
Traditionally, the problems of applying orthogonal matching pursuit (OMP) to large images are its high computing time and its requirement for a large matrix. In this paper, we propose a fast image recovery algorithm by dividing the image into block of n??n pixels and applying OMP to each n??n block instead of the entire image. The key idea is that small matrix requires less computing time and less...
Recent results in SRR (Super Resolution Reconstruction) demonstrate that the fusion of a sequence of low-resolution noisy blurred images can produce a higher-resolution image or sequence. Since noise is always present in practical acquisition systems, almost video enhancement algorithms are developed assuming AWGN model for the corrupting noise. When the underlying video measurements are corrupted...
Most video enhancement algorithms assume that the noise model of the imaging system is known as AWGN thereby imaging process model violations often occur since the real noise model is not known in many practical applications. Robust statistics has emerged as a family of theories and techniques for estimation while dealing with deviations from the idealized model assumptions. In this paper, we propose...
Many image video enhancements assume that the noise model of the imaging system is known in advance such as AWGN. However, the real noise model is not known in many practical applications. In this paper, we propose a novel robust video enhancement algorithm using SRR (super-resolution reconstruction) based on the stochastic regularization technique by minimizing a cost function. First, the classical...
Traditionally, the concept of super resolution reconstruction (SRR) relates to a process whereby images are obtained with resolutions that are beyond the limiting factors of the uncompensated imaging system. Many such SRR algorithms have been proposed during this decade but almost SRR estimations are based on L1 or L2 statistical norm estimation therefore these SRR algorithms are usually very sensitive...
In general, the classical SRR algorithms are usually based on translational observation model hence these SRR algorithms can be applied only on the sequences that have simple translation motion. In order to cope with real video sequences and complex motion sequences, this paper proposes a general observation model for SRR algorithm, fast affine block-based transform, devoted to the case of nonisometric...
Traditionally, Super Resolution Reconstruction (SRR) is the process by which additional information is incorporated to enhance a low resolution image thereby producing a high resolution image. This paper proposes the robust SRR algorithm for any noise model using the stochastic regularization technique by minimizing a cost function. The Geman&McClure norm is used for measuring the difference between...
Typically, super resolution reconstruction (SRR) is the process by which additional information is incorporated to enhance a noisy low resolution image hence producing a high resolution image. Although many such SRR algorithms have been proposed, almost SRR estimations are based on L1 or L2 statistical norm estimation hence these SRR algorithms are usually very sensitive to their assumed model of...
In these two decades, although there has been a great deal of research developing super-resolution reconstruction (SRR) algorithms and many such algorithms have been proposed, the almost SRR algorithms are based on L1 or L2 statistical norm estimation. Consequently, these SRR algorithms are typically very sensitive to their assumed noise model that limits their utility. This paper proposes a novel...
Within the traditional research community in this decade, there has been recently a great deal of work developing Super-Resolution Reconstruction (SRR) algorithms for combining a set of low quality images to produce a set of higher quality images. While many such algorithms have been proposed, the almost SRR estimations are based on L1 or L2 statistical norm estimation. Hence, these algorithms are...
This paper proposes a video enhancement method using a novel super-resolution reconstruction (SRR) framework for real standard sequences that are corrupted by any noise models. The traditional SRR algorithms are very sensitive to their assumed model of data and noise, which limits their utility. The real noise models that corrupt the measure sequence are unknown; consequently, SRR algorithm using...
Recently, there has been a great deal of work developing super-resolution reconstruction (SRR) algorithms. While many such algorithms have been proposed, the almost SRR estimations are based on L1 or L2 statistical norm estimation, therefore these SRR algorithms are usually very sensitive to their assumed noise model that limits their utility. The real noise models that corrupt the measure sequence...
The success of SRR algorithms is highly dependent on the accuracy of the model of the imaging process. When the data or noise model assumptions do not faithfully describe the measure data, the estimator performance degrades. Most noise models used in SRR algorithms are based on AWGN model at low power therefore SRR algorithms can effectively apply only on the image sequence that is corrupted by AWGN...
Super-resolution reconstruction (SRR) aims to produces one or a set of high-resolution (HR) images from a sequence of low-resolution (LR) images. Due to translational registration, super-resolution reconstruction can apply only on the sequences that have simple translation motion. This paper proposed a novel super-resolution reconstruction that that can apply on real sequences or complex motion sequences...
In this paper, we describe a multiscale image fusion method based on shift invariant wavelet transform for decrease misregistration problem of thermal and visible images. The shift invariant wavelet transform avoids the downsampling process. It leads to translation invariant which is available to image fusion. Additionally, the fuzzy possibilistic c-means clustering (FPCM) is applied to perform the...
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